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Enregistrement W4402404660 · doi:10.1097/ee9.0000000000000340

It takes a village: the Multi-Country Multi-City (MCC) Collaborative Research Network

2024· editorial· en· W4402404660 sur OpenAlexaboutno aff
Bert Brunekreef

Notice bibliographique

RevueEnvironmental Epidemiology · 2024
Typeeditorial
Langueen
DomaineEnvironmental Science
ThématiqueClimate Change and Health Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRegional scienceGeography

Résumé

récupéré en direct d'OpenAlex

A forthcoming “special collection” in Environmental Epidemiology highlights a series of new findings from the MCC study, the Multi-Country Multi-City (MCC) Collaborative Research Network. For almost 10 years now, this unique collaboration has raised the science of the short-term effects of weather and air pollution on population health to new levels. Research on the short-term effects of weather and air pollution on population health has a long history. An early report on the effects of very high air pollution levels on mortality describes a smog along the Meuse Valley in Belgium, which occurred in 1930.1,2 Interestingly, the original 1936 report almost quantitatively predicted the number of deaths that would occur in London under similar circumstances, which occurred in the famous December 1952 episode that led to the introduction of the first modern air pollution legislation in the world. Effects of especially high temperatures on health have also been studied for a long time. A remarkable report from 19383 documented a detailed breakdown of heat-related deaths by cause, age, sex, city size, and other modifiers in Massachusetts. The effects of excessive heat and air pollution on mortality are well documented (and fairly easy to document) but such early studies do not answer the question of whether nonexcessive temperatures and air pollution levels are still associated with mortality and other adverse health outcomes. Efforts to identify a threshold for the association between air pollution and mortality date back at least 40 years.4 Such studies require large populations as the effects at low concentrations, if any, are bound to be subtle. They also require sufficient days with low air pollution concentrations, which were rare in days of old when major cities with large enough populations across the world almost all suffered from high pollution levels. Such limitations also apply to studies of population health effects of subtle increases, or decreases, in day-to-day temperatures. And yes, the effects of heat waves and excessively high temperatures have generated more interest in recent decades than the effects of cold temperatures, but these are of equal interest. An early study from the Netherlands5 clearly showed that even in a country without very high or low temperature extremes, mortality increased when temperatures were below or above a narrow optimum temperature range of a few degrees Celsius above and below 16.5ºC. The 1990s saw an explosion of studies on short-term associations between air pollution and mortality. Soon, multicenter studies were organized to overcome the limitations of studying such associations in single cities, including the Air Pollution and Health: a European Approach (APHEA) in Europe,6 the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) in the United States,7 and a combined European–USA–Canadian effort.8 Interest in the acute effects of especially high temperatures strongly increased in the first decade of the 21st century, in response to the 2003 heatwave that killed tens of thousands of Europeans.9–11 Climate change is making serious heat waves ever more likely, and interest seems to have focused more on the effects of heat than on the effects of cold. Yet, excess winter mortality due to cold weather may be equally or even more important, especially in countries with mild climates not adapted to severe cold spells.12 As warm and cold weather on the one hand and high air pollution concentrations on the other hand tend to covary, an important question is whether their effects on population health can be separated. Early work from the Netherlands suggested air pollution (measured as SO2) effects were confounded by temperature effects.13 Later work has generally found that the effects of air pollution and weather were both important.14 The MCC study, highlighted in the special collection of articles in this volume of Environmental Epidemiology, is a (relatively) new kid on this very populous block of studies as they have emerged over the last several decades. It is in many ways a unique enterprise. It is based on the voluntary contributions of many scientists all over the world, who contribute their data and time to make the analyses of huge datasets possible. The MCC study has a much wider geographic coverage than previous multicenter studies, which were largely restricted to Europe and North America, in addition to a number of more modest contributions from South-East Asia.15 Over the years, the MCC study has broken new ground both in terms of methodology and topics that were addressed. It has produced many landmark articles, of which the Lancet paper on effects of high and low temperatures16 and the New England Journal of Medicine paper on short-term effects of particulate matter air pollution17 are just a few. The articles published in the special collection address topics such as the influence of weather and air pollution on COVID and on economic loss, the modifying role of land use, and the decadal change in minimum mortality temperature related to global warming. A full introduction to the MCC achievements is to be found in the companion paper by Gasparrini and colleagues.18 Perhaps the most unique feature of the MCC enterprise is that it is almost completely unfunded. It survives, no, flourishes, because of the trust and companionship among the many authors and collaborating centers, and because of the gentle and continuous leadership of the coordinating investigators. As they say: it takes a village to raise a child (in this case a worldwide village) and the MCC child, now about 10 years old, is doing very well! Conflicts of interest statement The author declares that there is no conflicts of interest with regard to the content of this report.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesÉtudes des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0130,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,003
Communication savante0,0000,000
Science ouverte0,0010,002
Intégrité de la recherche0,0020,005
Charge utile insuffisante (le modèle a refusé de juger)0,0070,011

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,150
Tête enseignante GPT0,433
Écart entre enseignants0,283 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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